Evaluating the impacts of autonomous cars on the capacity of freeways in Brazil using the HCM-6 PCE methodology
DOI:
https://doi.org/10.14295/transportes.v29i2.2444Keywords:
Autonomous vehicles, Capacity, HCM-6, CAF, Simulation, Passenger-car equivalentAbstract
This paper analyses the factors that affect the impact of autonomous vehicles (AVs) on the capacity of a freeway in Brazil using an adaptation of the HCM-6 procedure for truck PCE estimation. A version of Vissim, recalibrated to represent traffic streams and AVs on Brazilian freeways, was used to simulate more than 25,000 scenarios representing combinations of traffic (e.g., AV fleets, AV platoons, percentage of AVs and of heavy goods vehicles) and road (grades and number of lanes) characteristics. AV impacts on capacity were evaluated by means of the capacity adjustment factor (CAF) and a model to estimate CAF from control variables was fitted and validated. The results indicate increases of up to 30% in capacity with 60% of platooning-capable AVs. Statistical analyses show that the fraction of AVs in the stream and the proportion of platooning-capable AVs are the factors with the greatest impact on this increase in capacity.
Downloads
References
Al-Kaisy, A. and C. Durbin (2011). Platooning on two-lane two-way highways: An empirical investigation, Procedia – Social and Behavioral Sciences, v. 16, p. 329–339. DOI:10.1016/j.sbspro.2011.04.454
Alkim, T. P., G. Bootsma and S. Hoogendoorn (2007). Field operational test - The assisted driver. Intelligent Vehicles Symposium, p. 1198–1203, Istambul: IEEE. DOI: 10.1109/IVS.2007.4290281
Bethônico, F. C., F. J. Piva and J. R. Setti (2016). Calibração de microssimuladores de tráfego através de medidas macroscópi-cas. Anais do XXX Congresso de Pesquisa e Ensino em Transportes. Rio de Janeiro: ANPET. Available at <http://146.164.5.73:30080/tempsite/anais/documentos/2018/Trafego Urbano e Rodoviario/Trafego em Rodovias II/4_487_AC.pdf> (accessed in 27/3/2021)
Bierstedt, J., A. Gooze, C. Gray, J. Peterman, L. Raykin and J. Walters (2014). Next-generation vehicles effects of on travel de-mand and highway capacity. Technical Report, Fehr and Peers, USA. Available at <https://issuu.com/fehrandpeers/docs/fp_think_next_gen_vehicle_white_paper_final> (accessed in 27/3/2021)
Calvert, S. C., W. J. Schakel and J. W. van Lint (2017). Will automated vehicles negatively impact traffic flow? Journal of Ad-vanced Transportation, v. 17, id 3082781, DOI:10.1155/2017/3082781.
Carvalho, L. G. S. and J. R. Setti (2019). Calibration of the Vissim truck performance model using GPS data. Transportes, v. 27, n. 3, p. 131–143, DOI:10.14295/transportes.v27i3.2042
Dowling, R., G. List, B. Yang, E. Witzke and A. Flannery (2014) Incorporating truck analysis into the Highway Capacity Manual, National Cooperative Freight Research Program (NCFRP) Report 31. Washington: TRB DOI:10.17226/22311
Fries, R., Y. Qi and S. Leight (2017) How many times should I run the model? Performance measure specific findings from VISSIM models in Missouri. In: 96th Annual Meeting of Transportation Research Board. Washington: TRB. Available at <https://trid.trb.org/View/1437256> (accessed in 27/3/2021)
Gorter, M. (2015). Adaptive cruise control in practice a field study and questionnaire into its influence on driver, traffic flows and safety. Master Thesis, Delft. Available at <http://resolver.tudelft.nl/uuid:727070c8-c3c4-469f-a51a-a772ce683fa4>
(accessed in 27/03/2021)
List, G. F., B. Yang and N. M. Rouphail (2015). On the treatment of trucks for analysis of freeway capacity. Transportation Re-search Record, v. 2483, p. 120–129. DOI:10.3141/2483-14
Mahmassani H. S. (2016) Autonomous vehicles and connected vehicle systems: flow and operations considerations. Trans-portation Science, v. 50, n. 4, p. 1140–1162, DOI: 10.1287/trsc.2016.0712.
Makridis, M., K. Mattas, B. Ciuffo, M. A. Raposo, T. Toledo and C. Thiel (2018). Connected and automated vehicles on a freeway scenario. Effect on traffic congestion and network capacity. In 7th Transport Research Arena TRA, Vienna, 13 p. DOI: 10.5281/zenodo.1483132
Marcomini, L. A. and A. L. Cunha (2018). A comparison between background modelling methods for vehicle segmentation in highway traffic videos. arXiv:1810.02835 [cs.CV] – DOI: arXiv abs/1810.02835.
Martin-Gasulla, M., P. Sukennik and J. Lohmiller (2019). Investigation of the impact on throughput of connected autonomous vehicles with headway based on the leading vehicle type. Transportation Research Record, v. 2673, n. 5, p. 617–626. DOI: 0.1177/0361198119839989
Milanés, V. and S. E. Shladover (2014). Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transportation Research Part C, v. 48, p. 285–300. DOI: 10.1016/j.trc.2014.09.001
Papadoulis, A., M. Quddus, and M. Imprialou (2019). Evaluating the safety impact of connected and autonomous vehicles on motorways. Accident Analysis & Prevention, v. 124, p. 12–22. DOI:10.1016/j.aap.2018.12.019
PTV (2019). Vissim 11 - User Manual. Technical Report, Karlsruhe.
Shi, Y., Q. He and Z. Huang (2019). Capacity analysis and cooperative lane changing for connected and automated vehicles: entropy-based assessment method. Transportation Research Record, v. 2673, n. 8, p. 485–498. DOI:10.1177/0361198119843474.
Shladover, S. E. (2009). Cooperative (rather than autonomous) vehicle-highway automation systems. IEEE Intelligent Transportation Systems Magazine, v. 1, n. 1, p. 10–19. DOI: 10.1109/MITS.2009.932716
Shladover, S. E., D. Su and X.-Y. Lu (2012). Impacts of cooperative adaptive cruise control on freeway traffic flow. Transporta-tion Research Record, v. 2324, n. 1, p. 63–70. DOI: 10.3141/2324-08
Strand, N, J. Nilsson, I. Karlsson and L. Nilsson (2011). Exploring end-user experiences: self-perceived notions on use of adaptive cruise control systems. IET Intelligent Transport Systems, v. 5, n. 2, p. 134–140. DOI: 10.1049/iet-its.2010.0116
Sukennik, P. (2018). Micro-simulation guide for automated vehicles. PTV – CoExist, Technical Report, available at <https://www.h2020-coexist.eu/wp-content/uploads/2018/11/ D2.5-Micro-simulation-guide-for-automated-vehicles.pdf> (accessed in 27/3/2021)
TRB (2016). Highway Capacity Manual (6th ed.). Washington, D.C.: TRB.
Van Arem, B., C. J. G. Van Driel and R. Visser (2006). The impact of cooperative adaptive cruise control on traffic-flow charac-teristics. IEEE Transactions on Intelligent Transportation Systems, v. 7, n. 4, p. 429–436. DOI: 10.1109/TITS.2006.884615
Vanderwerf, J., S. Shladover and M. A. Miller (2004). Conceptual development and performance assessment for the deployment staging of advanced vehicle control and safety systems. Technical Report. Berkeley: University of California. Available at <https://escholarship.org/content/qt8hg3b55r/qt8hg3b55r.pdf> (visited on 15/3/2021)
Xiao-bao, Y. and Z. Ning (2007). Effects of the Number of Lanes on Highway Capacity, International Conference on Management Science and Engineering, p. 351–356, Harbin: IEEE. DOI: 10.1109/ICMSE.2007.4421872
Zhao, L. and J. Sun (2013). Simulation framework for vehicle platooning and car-following behaviours under connected-vehicle environment. Procedia – Social and Behavioral Sciences, v. 96, p. 914–924. DOI: 10.1016/j.sbspro.2013.08.105
Zhou, J., L. Rilett and E. Jones (2019a). Sensitivity analysis of speed limit, truck lane restrictions, and data aggregation level on the HCM-6 passenger-car equivalent estimation methodology for western U.S. conditions. Transportation Research Rec-ord, v. 2673, n. 11, p. 493–504. DOI: 10.1177/0361198119851451.
Zhou, J., L. Rilett, and E. Jones (2019b). Estimating passenger car equivalent using the HCM-6 PCE methodology on four-lane level freeway segments in western U.S. Transportation Research Record, v. 2673, n. 11, p. 529–545. DOI: 10.1177/0361198119851448
Alessandri, A., A. Di Febbraro, A. Ferrara and E. Punta (1998) Optimal Control of Freeways via Speed Signalling and Ramp Metering. Control Engineering Practice, v. 6, n. 6, p. 771–780. DOI: 10.1016/S0967-0661(98)00083-5.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Renan Favero, José Reynaldo Setti
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who submit papers for publication by TRANSPORTES agree to the following terms:
- Authors retain copyright and grant TRANSPORTES the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of this journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in TRANSPORTES.
- Authors are allowed and encouraged to post their work online (e.g., in institutional repositories or on their website) after publication of the article. Authors are encouraged to use links to TRANSPORTES (e.g., DOIs or direct links) when posting the article online, as TRANSPORTES is freely available to all readers.
- Authors have secured all necessary clearances and written permissions to published the work and grant copyright under the terms of this agreement. Furthermore, the authors assume full responsibility for any copyright infringements related to the article, exonerating ANPET and TRANSPORTES of any responsibility regarding copyright infringement.
- Authors assume full responsibility for the contents of the article submitted for review, including all necessary clearances for divulgation of data and results, exonerating ANPET and TRANSPORTES of any responsibility regarding to this aspect.